Wireless LAN compatible multi-input multi-output system

ABSTRACT

A system and method for enhancing the data rate of a WLAN. Through the deployment of a MIMO system, the data rate ordinarily expected of a SISO type system can be doubled. Yet, the MIMO system of the present invention can remain backward compatible with conventional WLAN standards implemented by typical SISO type system. In particular, the packet preamble of the MIMO packet of the present invention is similar to that of a conventional SISO system so as to be backward compatible with conventional SISO receivers. Additionally, the data model of the MIMO system can be configured to support the detection of symbols in the MIMO packet of the present invention. Importantly, the present invention can include a least squares soft-detector for use in a wireless LAN compatible MIMO system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/452,513, filed in the United States Patent and Trademark Office onJun. 18, 2003, the entirety of which is incorporated herein byreference.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

The invention was made with U.S. government support under grant numberCCR-0097114 awarded by the National Science Foundation. The U.S.government may have certain rights in the invention.

STATEMENT OF THE TECHNICAL FIELD

The present invention relates to a multi-input multi-output (MIMO)system and more particularly to a MIMO system which is configured withbackward compatibility with wireless LAN (WLAN) technology.

DESCRIPTION OF THE RELATED ART

Orthogonal frequency-division multiplexing (OFDM) serves as the basisfor the Institute of Electrical and Electronic Engineers (IEEE) 802.11ahigh-speed WLAN standard as well as the IEEE 802.11g and HIPERLAN/2standards. The OFDM based WLAN system, as specified by IEEE 802.11a (theIEEE 802.11g and HIPERLAN/2 standards are similar to the IEEE 802.11astandard in the sense that they have the same signal format andgeneration; we use IEEE 802.11a to exemplify our presentation in thesequel) utilizes packet-based data transmission techniques. In thatregard, as shown in FIG. 5, each packet in the IEEE 802.11a scheme caninclude an OFDM packet preamble, a SIGNAL field and an OFDM DATA field.The packet preamble can be used to estimate necessary channel parameterssuch as carrier frequency offset (CFO), symbol timing, and channelimpulse response. These channel parameters are required for data symboldetection in the OFDM DATA field.

Notably, the preamble configuration specified by the 802.11a standardhas been designed specifically for a single-input single-output (SISO)system in which both the transmitter and receiver manage merely a singlesignal. In a SISO system configuration, then, the 802.11a standardsupports a data rate of at most fifty-four (54) mega-bits per second(Mbps). Yet, transmission data rates which exceed 54 Mbps can be ofparticular importance with respect to future WLAN implementations.

Deploying multiple antennae at both the transmitter and receiver hasbeen proven to be a promising way to achieve higher data transmissionrates for wireless communication systems over multipath-rich wirelesschannels, all the while without increasing the total transmission poweror bandwidth consumed by the system. In a system which incorporatesmultiple transmit and receive antennae, referred to in the art as a MIMOwireless communications system, a set of M transmit antennae, and a setof N receive antennae can be provided, wherein N and M are notnecessarily equal. Among the various popular MIMO systems known in theart, the Bell Labs Layered Space-Time (BLAST) system is a popularimplementation.

The BLAST system has been proven successful in achieving a potentiallylarge channel capacity offered by the MIMO system. In the BLAST system,the data stream can be de-multiplexed into multiple independentsub-streams referred to as layers. Each layer can be transmittedsimultaneously with other layers, for instance, using one antenna perlayer. At the receiver, the multiple layers can be detected. Thedetection function can be provided through successive detection via aninterference cancellation and nulling algorithm (ICNA), or possiblythrough a sphere decoding (SPD) algorithm.

Notably, in a system which conforms to the IEEE 802.11a standard, it canbe preferable to incorporate within the system the use of a channelcoding technique to provide the advantages of forward error correction(FEC), where channel coding is the process of adding redundantinformation to a digital signal conveyed through a channel. Two forms ofchannel coding include convolutional coding and block coding, withconvolutional coding being preferred. The IEEE 802.11a standard uses FECcodes so as to dramatically lower the Bit-Error-Rate (BER).Consequently, a convolutional decoder can be deployed within thereceiver of the BLAST system in satisfaction of this preference.

Importantly, it has been recognized by those skilled artisans that aconvolutional decoder with soft-information can outperform itscounterpart with hard-information. To that end, the Viterbiconvolutional decoder, a popular convolutional decoder, requiressoft-information in order to improve decoding performance. Yet, both theICNA and SPD schemes, in addition to the numerous variations thereof,can be characterized merely as “hard” detectors. Consequently, neitherthe ICNA scheme, nor the SPD scheme can provide the soft informationrequired by the Viterbi decoder.

Though attempts have been made to design soft-detectors based on theICNA and SPD schemes, the known designs have been characterized ascomplicated and not suitable for wide-spread use. Similarly, whilespace-time bit interleaved coded modulation (STBICM) technology candeliver the soft-output required by the Viterbi decoder, this can bedone only at a tremendous computational cost when using large symbolconstellations, such as 64-QAM, even when the number of transmittingantennas or M is as small as 2. Finally, a list SPD (LSPD) algorithm hasbeen proposed for reducing the computational cost of STBICM with a priceof only a small performance degradation. Yet, LSPD technology is stillcomplicated to implement, particularly in reference to real-timeimplementations involving large constellations.

SUMMARY OF THE INVENTION

The present invention is a system and method for enhancing the data rateof a WLAN. In particular, through the deployment of a MIMO system, thedata rate ordinarily expected of a SISO type system can be doubled. Yet,the MIMO system of the present invention can remain backward compatiblewith conventional WLAN standards implemented by typical SISO typesystem. In particular, the packet preamble, referred to as a MIMOpreamble, of the MIMO packet of the present invention is similar to thatof a conventional SISO system so as to be backward compatible with theconventional SISO packet preamble. Additionally, the data model of theMIMO system can be configured to support the detection of symbols in theMIMO packet of the present invention.

Importantly, the present invention can include a least squaressoft-detector for use in a wireless LAN compatible MIMO system. Inaccordance with the inventive arrangements, an unstructured leastsquares estimate can be produced for a MIMO time-invariant flatRayleigh-fading channel. Additionally, a marginal probability densityfunction can be computed for the unstructured least squares estimate.Based upon the marginal probability density function, the variance canbe computed for the channel. Finally, both the unstructured leastsquares estimate and the variance collectively can be used to provide aViterbi convolutional decoder the required soft-information.

BRIEF DESCRIPTION OF THE DRAWINGS

There are, shown in the drawings, embodiments which are presentlypreferred; it is understood, however, that the invention is not limitedto the precise arrangements and instrumentalities shown, wherein:

FIG. 1 is schematic illustration of a MIMO system configured with thesoft-detector of the present invention;

FIG. 2 is a schematic illustration of a MIMO transmitter for use in theMIMO system of FIG. 1;

FIG. 3 is a schematic illustration of a MIMO receiver for use in theMIMO system of FIG. 1;

FIG. 4 is a flow chart illustrating a process for acquiring andcomputing soft information for use in the decoder of FIG. 3;

FIG. 5 is a timing diagram illustrating the conventional IEEE 802.11apacket structure known in the art; and,

FIG. 6 is a timing diagram illustrating a MIMO packet structure for usein the MIMO system of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is a system and method for enhancing the data rateof a WLAN. Specifically, in the present invention, two or more crosseddipole antennas can be disposed both at the transmit and receive ends ofthe MIMO system. A MIMO transmission packet further can be providedwhich can include a MIMO preamble, SIGNAL field and OFDM DATA field inthe same regard as a conventional SISO system transmission packet likethe IEEE 802.11a transmission packet. Similarly, a carrier frequencyoffset estimation method, symbol timing method, and channel responsemethod can be provided for the MIMO preamble. Finally, a MIMOsoft-detector can be provided for bit-level detection.

Notably, the present invention can include a novel and non-obvioussoft-detector for use in the MIMO system described herein. In thesoft-detector of the present invention, unstructured least squareestimates can be produced for received symbols in a MIMO systemreceiver. Additionally, signal-to-noise ratio can be computed for thereceived symbols. Subsequently, the produced unstructured least squareestimates and the computed SNR are collectively used to providesoft-information to the decoder portion of the MIMO system receiver sothat the decoder portion can decode the received signal while permittingonly a minimum bit error rate.

Importantly, the decoder portion can be a Vitirbi convolutional decoderconfigured for use with soft-information rather than hard-informationproduced by a hard detector such as an ICNA or SPD scheme. Consequently,in the MIMO system described herein, hard information produced by ahard-detector will not be required.

FIG. 1 is schematic illustration of a MIMO system configured with thesoft-detector of the present invention. In a preferred aspect of theinvention, the MIMO system can include a BLAST transmitter 130communicatively coupled to a BLAST receiver 160. The BLAST transmitter130 can process data 110 through a convolutional encoder 120 to produceencoded data formatted for wireless transmission via an array of Mantennae 140.

The wirelessly transmitted packets can be formatted for backwardcompatibility with conventional WLAN packet formatting protocols. Thoughthe preamble design can be used with two transmission antennae 140 andan unlimited number of receiving antennae 150, two pairs of eachantennae type can be suitable. In this regard, the packets can betransmitted simultaneously from the transmit antennae 140, each packethaving a SISO-like preamble. Referring to FIG. 5 for a moment, for anIEEE 802.11a based SISO system, as an example, short training symbolscan be used to detect the arrival of the packet, to allow the automaticgain control to stabilize, to compute a coarse CFO estimate, and toobtain a coarse symbol timing. Long training symbols, by comparison, canbe used to calculate a fine CFO estimate, refine the coarse symboltiming, and estimate the SISO channel.

FIG. 6 is a timing diagram illustrating a MIMO packet structure for usein the MIMO system of the present invention. By comparison to the packetstructure of FIG. 5, initially it can be presumed that the receiverantennae outputs of the MIMO system suffer from the same CFO and havethe same symbol timing. Thus, as shown in FIG. 6, to ensure backwardcompatibility with a conventional SISO arrangement, identical shorttraining symbols can be used in the MIMO system of the present inventionas in the SISO preamble for the transmit antennae of the MIMO system. Inrespect to channel estimation, an orthogonal training sequence can befurther incorporated in the preamble design of the MIMO system of thepresent invention.

In the interest of backward compatibility, both T1 and T2 can remain thesame as in the case of the conventional SISO system, (as well as GI2)for both of the MIMO system transmit antennae before the SIGNAL field,as shown in FIG. 6. After the SIGNAL field, however, an additional T1,T2 and GI2 can be provided for a first transmit antenna, while a −T1,−T2 and −GI2 can be provided for the other transmit antenna. In thisway, when simultaneously transmitted packets received by a single SISOreceiver, the SISO receiver can successfully detect up to the SIGNALfield, which remains the same for both transmit antennae. Using thereserved bit within the SIGNAL field, the SISO receiver can stop itsoperation whenever a MIMO transmission follows. Otherwise, the SISOreceiver can continue its operation.

The long training symbols shown in FIG. 6 as both before and after theSIGNAL field can be re-used in MIMO receivers for channel estimation.Once again, an inspection of the reserved bit in the SIGNAL field caninform the MIMO receiver that the transmission is a SISO transmission.Upon detecting a SISO transmission, the MIMO receiver can modify itschannel estimation and data bit detection steps to accommodate the SISOtype transmission.

Importantly, one skilled in the art will recognize that many other MIMOpreamble designs can ensure backward compatibility with conventionalSISO systems and the foregoing description of a specific MIMO packetstructure is not to be taken as the exclusive manner in which a MIMOpacket can be configured for backward compatibility. For example, byexploiting the transmit/receive diversities, one can attain improvedsymbol timing or CFO correction. However, these improvements do notnecessarily result in improved packet error rate (PER). Hence, at leastin a preferred embodiment, the straightforward MIMO preamble design ofFIG. 6 can suffice.

To stay as close to the IEEE 802.11a standard as possible, each of ascrambler, convolutional encoder, puncturer, FD (frequency domain)interleaver, symbol mapper, pilot sequence, and CP (cyclic prefix) canbe included in the MIMO system in the same way as specified in aconventional SISO system such as IEEE 802.11a. To improve diversity, asimple spatial interleaver can be added to scatter every two consecutivebits across the two transmit antennas. In particular, the spatialinterleaving can be performed before the FD interleaving.

Mathematically, then, consider the n_(s) th subcarrier (for notationalconvenience, we drop the notational dependence on n_(s) below). Considerthe case of N receive antennas. (Note that considering the general caseof N receive antennas does not add extra difficulties for thediscussions below.) Let H denote the MIMO channel matrix for the n_(s)th subcarrier:

$H = {\begin{bmatrix}h_{1,1} & h_{1,2} \\h_{2,1} & h_{2,2} \\\vdots & \vdots \\h_{N,1} & h_{N,2}\end{bmatrix} \in C^{Nx2}}$where h_(n,m) denotes the channel gain from the mth transmit antenna tothe nth receive antenna for the n_(s) th subcarrier. Let y denote areceived data vector for the n_(s) th subcarrier, which can be writtenas y=Hx+eεC^(Nx1) where x=[x₁x₂]^(T) is the 2×1 QAM symbol vector senton the n_(s) th subcarrier and e≈N(0,σ²I_(N))is the additive whitecircularly symmetric complex Gaussian noise with variance or σ².

Notably, applying the MIMO preamble packet structure of FIG. 6,sequential CFO, symbol timing, and MIMO channel estimation can beundertaken as follows. Specifically, the channel estimates can beobtained in the foregoing order.

Let z_(n)(l)=z_(n) ^(ne)(l)+e_(n)(l), n=1, . . . , N denote the lth timesample of the signal received from the nth receive antenna, startingfrom the moment that the receiver AGC has become stationary (thereceiver AGC is assumed to become stationary at least before receivingthe last two short OFDM training symbols and remain stationary whilereceiving the remainder of the packet).

In the presence of CFO, ε, we have z_(n) ^(ne)(l+N_(C))=z_(n)^(ne)(l)e^(j2N) ^(c) ^(πε), n=1, . . . ,N. For each receive antennaoutput, consider the correlation between two consecutive noise-freereceived data blocks, each of which is of length N_(C). Then the sum ofthe correlations for all receive antennas can be written as

${\sum\limits_{n = 1}^{N}{\sum\limits_{l = k}^{k + N_{C} - 1}{{z_{n}^{ne}(l)}\left( {z_{n}^{ne}\left( {l + N_{C}} \right)} \right)^{*}}}} = {{{\mathbb{e}}^{{- j}\; 2N_{C}\pi\; ɛ}{\sum\limits_{n = 1}^{N}{\sum\limits_{l = 0}^{N_{C} - 1}{{z_{n}^{ne}(l)}}^{2}}}} \equiv {P\;{\mathbb{e}}^{{- j}\; 2N_{C}\pi\; ɛ}}}$where (.)* denotes the complex conjugate and k is any non-negativeinteger such that z_(n) ^(ne)(k+2N_(C)−1) is a sample of the nth receiveantenna output due to the input (transmit antenna output) being a sampleof the short OFDM training symbols of the MIMO packet preamble. Let

${\sum\limits_{n = 1}^{N}{\sum\limits_{l = 0}^{N_{C} - 1}{{z_{n}(l)}{z_{n}^{*}\left( {l + N_{C}} \right)}}}} = {{P\;{\mathbb{e}}^{{- j}\; 2N_{C}\pi\; ɛ}} + e_{P}}$where e_(P) is due to the presence of the noise. Based upon theforegoing, the coarse CFO can be computed according to

${\hat{ɛ}}_{C} = {{- \frac{1}{2\; N_{C}\pi}}\angle\; P_{S}}$where ∠x denotes taking the argument of x.

The course CFO can be corrected using the computed {circumflex over(ε)}_(C) to obtain data samples z_(n) ^((C))(l), n=1,2, . . . , N, usingthe equation z_(n) ^((C))(l)=z_(n)(l)e^(j2lπ{circumflex over (ε)}) ^(c). Correspondingly, the following will be recognized by the skilledartisan: P_(S) ^((C))=P_(S)e^(j2lπ{circumflex over (ε)}) _(C). Notably,in the sequel, only the CFO corrected data given above need to beconsidered. Moreover, for notational convenience the superscript ofz_(n) ^((C))(l), n=1,2, . . . , N can be dropped.

As it will be recognized by the skilled artisan, symbol timing has beendefined as the starting time sample due to the input being the long OFDMtraining symbol T1 (before the SIGNAL field). Once the starting timesample due to the long OFDM training symbol T1 is determined, thestarting time sample due to every OFDM symbol thereafter can bedetermined, as it is specified in conventional WLAN specifications suchas the IEEE 802.11a standard. In any case, the coarse symbol timing canbe computed in iterative fashion according to:

$\begin{matrix}{{P_{R}\left( {k + 1} \right)} = {{P_{R}(k)} +}} \\{{Re}\left\{ {\sum\limits_{n = 1}^{N}\left\lbrack {{{z_{n}\left( {k + N_{C}} \right)}{z_{n}^{*}\left( {k + {2N_{C}}} \right)}} - {{z_{n}(k)}{z_{n}^{*}\left( {k + N_{C}} \right)}}} \right\rbrack} \right\}} \\{= {{P_{R}(k)} +}} \\{\sum\limits_{n = 1}^{N}\left\{ {{{{\overset{\sim}{z}}_{n}\left( {k + N_{C}} \right)}\left\lbrack {{{\overset{\sim}{z}}_{n}\left( {k + {2N_{C}}} \right)} - {{\overset{\sim}{z}}_{n}(k)}} \right\rbrack} +} \right.} \\\left. {{{\overset{\sim}{z}}_{n}\left( {k + N_{C}} \right)}\left\lbrack {{{\overset{\sim}{z}}_{n}\left( {k + {2N_{C}}} \right)} - {{\overset{\sim}{z}}_{n}(k)}} \right\rbrack} \right\}\end{matrix}$where both Re(●) and ({overscore (●)}) denote the real portion of acomplex entity and ({tilde over (●)}) reflects the imaginary portion.From the foregoing computations, the skilled artisan will recognize thatthe correlation after the CFO correction can be approximated as thereal-valued scalar P in addition to a complex-valued noise. In thisiterative approach, the iteration can begin using P_(R)(0)=Re(P_(S)). Itwill be noted that, when compared to conventional correlativetechniques, the real-valued correlation approach stated aboveincorporates fewer computations, lowers the noise level (variancereduced in half) in the correlation sequence, and decreases closer tozero as the data samples in the sliding data blocks are due to the inputbeing GI2 or the long OFDM training symbols following the short OFDMtraining symbols in the MIMO preamble.

When some of the data samples of the sliding data blocks are taken fromthe received data due to the input being GI2 or the long trainingsymbols following the short OFDM training symbol, P_(R)(k) will drop.This dropping property can be used to obtain the coarse symbol timing.

Let T_(P) denote the first time sample when P_(R)(k) drops to below halfof its peak value. The coarse symbol timing can be expressed as

${T_{C} = {T_{P} + {\frac{3}{2}N_{C}} + N_{C}}},$where T_(C) is the coarse estimate of the beginning time sample due tothe input being the long OFDM training symbol T1 before the SIGNALfield. The second term at the right hand side of the above equation isdue to the fact that P_(R)(k) will drop to approximately one half of itsmaximum value when the data samples of the second half of the second ofthe two sliding blocks are due to the first GI2 in the MIMO preamble asinput GI2; the third term is due to one half of the length of GI2. Whenthe channel spreading delay t_(D)=max{τ_(p)−τ₁} is assumed to satisfyt_(D)≦N_(C), only the first half of GI2 can suffer from inter-symbolinterference. Hence it is preferred to place the coarse timing estimatebetween the true timing T₀=193 and T₀−N_(C)=177 to produce as accurate afine CFO estimation as possible.

For each receive antenna output, a correlation between the two longtraining symbols before the SIGNAL field can be computed. Thecorrelations subsequently can be summed for all receiving antennas asfollows:

$P_{L} = {\sum\limits_{n - 1}^{N}{\sum\limits_{l = 0}^{N_{S} - 1}{{z_{n}\left( {l + T_{C}} \right)}{z_{n}^{*}\left( {l + T_{C} + N_{S}} \right)}}}}$

Consequently, the fine CFO estimate can be computed as

${\hat{ɛ}}_{F} = {{- \frac{1}{2N_{S}\pi}}\angle\;{P_{L}.}}$It will be recognized by the skilled artisan that the fine CFO estimatecan be used in the same manner as the course CFO estimate to correct theCFO. It can be important to note that the fine CFO estimation isaccurate enough for the following fine symbol timing estimation and MIMOchannel response estimation. Yet, the fine CFO estimation can never beperfect due to the presence of noise. Hence before data bits detection,it is preferable to use the pilot symbols to track the CFO residualphase for each OFDM data symbol.

Once the CFO has been corrected with the fine CFO estimate, the finesymbol timing can be obtained through the use of N data blocks of lengthN_(S) corresponding to the N receive antennas, starting from the timesample T_(C). In this regard, let y_(n) denote the N_(S)-point FFT ofthe data block from the nth receive antenna. Furthermore, let h_(n,m)^((t)) be the equivalent FIR channel in the time-domain between the mthtransmit antenna and the nth receive antenna, n=1, 2, . . . , N, andm=1, 2. Then, by neglecting the existence of the residual CFO, y_(n) canbe written as

$y_{n} = {{S_{B}W_{N_{S}}{\sum\limits_{m = 1}^{2}h_{n,m}^{(t)}}} + {W_{N_{S}}e_{n}}}$where S_(B) is a diagonal matrix with the fifty-two known BPSK symbolswhich form T1 of FIG. 6, and twelve zeros on the diagonal. Since theMoore-Penrose pseudo-inverse of S_(B) is S_(B) itself and W_(N) _(S)/N_(S) ^(1/2) is unitary, an estimate of ĥ_(n) ^((t))=Σ_(m=1) ²h_(n,m)^((t)) can be produced as

${\hat{h}}_{n}^{(t)} = {\frac{1}{N_{S}}W_{N_{S}}^{H}S_{B}{y_{n}.}}$Let T₁ denote the index of the first element of Σ_(n=1) ^(N)|ĥ_(n)^((t))| that is above one-third of the maximum value of the elements ofΣ_(n=1) ^(N)|ĥ_(n) ^((t))|. Then, the fine symbol timing T_(F) can bedefined as T_(F)=T_(C)+T₁−3. Importantly, the constant 3 can be chosento ensure that T_(F)>T₀.

After T_(F) has been obtained, the MIMO channel response can beestimated. Specifically, y_(n,1) can be allowed to denote theN_(S)-point FFT of the average of the two consecutive blocks, each ofwhich is of length N_(S), associated with the two long training symbolsbefore the SIGNAL field, from the nth receive antenna. Correspondingly,y_(n,2) can be allowed to denote the counterpart of y_(n,1) after theSIGNAL field. Thus, for the n_(S) th sub-carrier, the following can holdtrue:y _(n,1) ≈s _(B)(h _(n,1) +h _(n,2)),t _(n,2) ≈s _(B)(h _(n,1) −h _(n,2))where s_(B) denotes the n_(s) th diagonal element of S_(B), y_(n,i)denotes the n_(s) th element of y_(n,1), i=1 and wherein the dependenceupon n_(S) has been dropped for notational simplicity. Solving for bothequations can yield the channel estimates:ĥ _(n,1) =s _(B)(y _(n,1) +y _(n,2))/2,ĥ _(n,2) =s _(B)(y _(n,1) −y _(n,2))/2When the reserved bit in the SIGNAL field indicates a SISO transmission,however, only h_(n,1), n=1,2, . . . , N need be solved.

Returning now to FIG. 1, with the channel parameters such as CFO, symboltiming, and MIMO channel determined and corrected, the data bitscontained in each layer in the OFDM DATA field can be detected. To thatend, the received packets can be subjected to a convolutional decodingprocess 190 to produce the data 110 with a minimum bit error rate.Significantly, the convolutional decoding process 190 can receivesoft-information in order to improve the decoding performance. Morespecifically, an unstructured least squares estimation process 170 cancompute a set of unstructured least squares estimates based upon thechannel gain between each of the transmitting antennae 140 and thereceiving antennae 150.

In addition, a SNR determination process 180 can produce the SNR on eachsubcarrier. The combined output of the unstructured least squaresestimation process 170 and the SNR determination process 180 can becollectively used to obtain the “soft-information” necessary for theeffective operation of the convolutional decoding process 190.

FIG. 2 is a schematic illustration of a MIMO transmitter for use in theMIMO system of FIG. 1. The MIMO transmitter can include a convolutionalencoder 210, a bit interleaver 220, a 1 to M Demultiplexer 230, and MModulators 240 coupled to corresponding antennae. Initially, theconvolutional encoder 210 can receive a block of size L bits, b={b₁,b₂,. . . , b_(L)}ε{0,1}^(1xL) as its input and can produce a larger blockof bits of size {hacek over (L)} representing the coded signal:C(b)={u ₁ ,u ₂ , . . . , u _(L)}ε{0,1}^(1xL).Based upon the coding of the signal C(b), the coding rate can be definedas

$R_{C} = {\frac{L}{\overset{\Cup}{L}}.}$Still, as it will be recognized by one skilled in the art, it can bepreferable to puncture the output block u to obtain a smaller block ofbits of size {tilde over (L)}v={v ₁ , v ₂ , . . . , v_({tilde over (L)})}ε{0,1}^(1x{tilde over (L)})({tilde over (L)}<{hacekover (L)})to increase the data rate. Accordingly, the puncturing rate can beexpressed as:

$R_{P} = {\frac{\overset{\sim}{L}}{\overset{\Cup}{L}}.}$Hence the coding rate of the punctured output can be expressed as:

$R_{P} = {\frac{R_{C}}{R_{P}}.}$

Once punctured, the output v of the convolutional coder 210 can beforwarded to the interleaver 220. The interleaver can produce the outputv′={v ⁽¹⁾ ,v ⁽²⁾ , . . . , v^(({tilde over (L)}))}ε{0,1}^(1x{tilde over (L)})which can be subsequently fed to the 1 to Mdemultiplexer 230. Notably,as

$L^{\prime} = {\frac{\overset{\sim}{L}}{M} = {KN}_{CBPS}}$is an integer, and the output of the 1 to M demultiplexer 230 caninclude M independent layers. Here K is an integer and NCBPS is thenumber of coded bits per data OFDM symbol. Each layer can be denoted asa block of bits of size L′:v′ _(m) ={v _(m) ⁽¹⁾ ,v _(m) ⁽²⁾ , . . . , v _(m) ^((L′))}ε{0,1}^(1xL′),m=1,2, . . . , M

Each modulator 240 can map each layer of the binary bits into data OFDMsymbols in the same way as a conventional SISO OFDM modulator.

FIG. 3 is a schematic illustration of a MIMO receiver for use in theMIMO system of FIG. 1. The MIMO receiver (i.e., the BLAST receiver 160)can include a demodulator 310 coupled to N antennae, an M to 1multiplexer 320, a deinterleaver 330 and a convolutional decoder 340 or190. Consider the nth subcarrier for the kth, k=1, . . . , K, data OFOMsymbol. (For national convenience, we drop the dependence on n_(S) andk.) The demodulator 310 (i.e., the unstructured least-squares detector170) initially can generate a soft-decision statistic {circumflex over(x)}_(m) for each transmitted QAM symbol x_(m), m=1,2, . . . , M. Thestatistic can be expressed as {circumflex over (x)}_(m)=x_(m)+e_(m)where e_(m) is assumed to be a circularly symmetric independentlydistributed complex Gaussian error with zero-mean and variance (σ_(m))².Notably, the assumption set forth herein can be ensured through theinterleaving. In any case, the pair of equations, ({circumflex over(x)}_(m),σ_(m)) can be used to obtain “soft information” associated withthe received data of the transmitted QAM symbol x_(m).

Significantly, the demodulator 310 can map the soft-information toobtain the bit metric: {{circumflex over (v)}_(m) ⁽¹⁾,{circumflex over(v)}_(m) ⁽²⁾, . . . , {circumflex over (v)}_(m) ^((B))} for the binarybits x mapped to the QAM symbol x_(m). More particularly, as the realand imaginary portions of the QAM symbol are orthogonal to each otherand the real and imaginary portions of the additive complex Gaussiannoise are shown to be independent of each other, the soft-decisionstatistic can be mathematically divided into real and imaginary partscorresponding to the soft-decision statistic of two real valued pulseamplitude modulated (PAM) symbols. The variance of the noise additive tothe PAM symbols can be halved as compared to the QAM symbols.Consequently, the bit metric can be calculated for each symbol in a PAMconstellation.

In this regard, where D_(ij)={s: sε

} denotes the set of all possible PAM symbols for the PAM constellation

, with the i th bit v_(i)=j,i=1,2, . . . , B/2, j=0,1, the formation ofD_(i,j) can depend upon the manner in which the PAM symbols are labeled.In any case, for a given set of soft-information (x,σ) for the PAMsymbol, the bit metric for v_(i) can be given by:

${{\hat{v}}_{i} = {\log\mspace{11mu}\frac{p_{\sigma}\left( {v_{i} = \left. 1 \middle| x \right.} \right)}{p_{\sigma}\left( {v_{i} = \left. 0 \middle| x \right.} \right)}}},{where}$${p_{\sigma}\left( {v_{i} = \left. j \middle| x \right.} \right)} = {{p_{\sigma}\left( D_{i,j} \middle| x \right)} = {{\sum\limits_{s \in D_{i,j}}^{\;}{p_{\sigma}\left( s \middle| x \right)}} = {\sum\limits_{s \in D_{i,j}}^{\;}\frac{{f_{\sigma}\left( x \middle| s \right)}{p(s)}}{p(x)}}}}$with

${f_{\sigma}\left( x \middle| s \right)} = {\frac{1}{\sqrt{{2\;\pi}\;}\sigma}\mspace{11mu}{\mathbb{e}}^{\frac{{({x - 2})}^{2}}{2\;\sigma^{2}}}}$as the probability density function given the symbol x and the varianceσ².

The occurrence of each symbol in

can be assumed to be equally as likely which can be expressedmathematically as p(s)=(½)^(B/2),∀sε

. Consequently, p_(σ)(v_(i)=j|x) can be expressed as

$\frac{1}{2^{B/2}{p(x)}}\mspace{11mu}{\sum\limits_{s \in D_{i,j}}{f_{\sigma}\left( x \middle| s \right)}}$which can lead to the identity:

${\hat{v}}_{i} = {{\log\mspace{11mu}\left\{ {\sum\limits_{s \in D_{i,1}}{\mathbb{e}}^{\frac{{({x - s})}^{2}}{2\;\sigma^{2}}}} \right\}} - {\log\mspace{11mu}{\left\{ {\sum\limits_{s \in D_{i,0}}{\mathbb{e}}^{\frac{{({x - s})}^{2}}{2\;\sigma^{2}}}} \right\}.}}}$Notably, to enhance the speed of computing the bit metric, practically agrid can be pre-computed for varying sets of x and σ. The grid can bearranged in tabular format as a look-up table for selected v_(i)s.

Returning now to FIG. 3, presuming that the output of the demodulator310 can be expressed as v″_(m)={{circumflex over (v)}_(m) ⁽¹⁾,{circumflex over (v)}_(m) ⁽²⁾, . . . , {circumflex over (v)}_(m)^((L′))}, m=1,2, . . . , M for the bit metric sequence corresponding tothe Mth transmitted layer, the M bit metric sequences can be combined bythe M to 1 Multiplexer 320 to produce the longer bit metric sequencev″={{circumflex over (v)}⁽¹⁾,{circumflex over (v)}⁽²⁾, . . . ,{circumflex over (v)}^(({tilde over (L)}′))}. Passing v″ through thedeinterleaver 330, a deinterleaved bit metric sequence can be producedaccording to the equation v′={{circumflex over (v)}₁,{circumflex over(v)}₂, . . . , {circumflex over (v)}_({tilde over (L)})}. Subsequently,a zero bit metric can be applied to each punctured bit prior to applyingthe Viterbi decoding algorithm to the bit sequence. The application ofthe zero bit metric can produce the bit sequence û={û₁,û₂, . . . ,û_({tilde over (L)})} corresponding to the encoder output u.Subsequently, applying the Viterbi decoding algorithm to the sequence u,an estimated bit sequence can be produced for the original source binarybit sequence, b.

FIG. 4 is a flow chart illustrating a process for acquiring andcomputing soft information for use in the decoder of FIG. 3. The processcan begin in block 410 in which the comparative gain for each subcarriercan be obtained for each pair of M transmit and N receive antennae inthe MIMO system. In block 420, the comparative gains can be written inmatrix form to obtain

$H = {\begin{bmatrix}h_{1,1} & h_{1,2} & \cdots & h_{1,M} \\h_{2,1} & h_{2,2} & \cdots & h_{2,M} \\\vdots & \vdots & ⋰ & \vdots \\h_{N,1} & h_{N,2} & \cdots & h_{N,M}\end{bmatrix} \in C^{N \times M}}$where h_(n,m) is the gain from the mth transmit antenna to the nthreceive antenna on the n_(s)th subcarrier. (We drop n_(s) forconvenience.) With x=[x₁ x₂ . . . x_(M)]^(T) denoting the M×1 QAM symbolvector transmitted at the time {overscore (l)}, the received signal canbe expressed as y=H x+nεC^(Nx1) where n≈N (0,σ_(i) ²I_(N)) can representthe additive white circularly symmetric complex Gaussian noise.

Subsequently, in block 430, the unstructured least squared estimate canbe established as {circumflex over (x)}_(us)=x+(H^(H)H)⁻¹H^(H)n=x+e. Byignoring the dependence among the elements of e, we can consider onlythe marginal probability density function for the unstructured leastsquares estimate {circumflex over (x)}_(us)(m), m=1,2, . . . , M

${\left( {H^{H}H} \right)^{- 1}H^{H}} = {\begin{bmatrix}{\overset{\Cup}{h}}_{1}^{T} \\{\overset{\Cup}{h}}_{2}^{T} \\\vdots \\{\overset{\Cup}{h}}_{M}^{T}\end{bmatrix} \in C^{M \times N}}$Then the mth element of e, e_(m), with m ranging from 1 to M, can beexpressed as e_(m)={hacek over (h)}_(m) ^(T)n. As such, in block 440 thevariance can be computed as σ_(m) ²=E[|e_(m)|²]=∥{hacek over(h)}_(m)∥²σ². Finally, in block 450 the variance along with theunstructured least squares estimate can be provided to the decoder asthe required soft information.

The method of the present invention can be realized in hardware,software, or a combination of hardware and software. An implementationof the method of the present invention can be realized in a centralizedfashion in one computer system, or in a distributed fashion wheredifferent elements are spread across several interconnected computersystems. Any kind of computer system, or other apparatus adapted forcarrying out the methods described herein, is suited to perform thefunctions described herein.

A typical combination of hardware and software could be a generalpurpose computer system with a computer program that, when being loadedand executed, controls the computer system such that it carries out themethods described herein. The present invention can also be embedded ina computer program product, which comprises all the features enablingthe implementation of the methods described herein, and which, whenloaded in a computer system is able to carry out these methods.

Computer program or application in the present context means anyexpression, in any language, code or notation, of a set of instructionsintended to cause a system having an information processing capabilityto perform a particular function either directly or after either or bothof the following a) conversion to another language, code or notation; b)reproduction in a different material form. Significantly, this inventioncan be embodied in other specific forms without departing from thespirit or essential attributes thereof, and accordingly, referenceshould be had to the following claims, rather than to the foregoingspecification, as indicating the scope of the invention.

1. A method of wirelessly conveying data comprising the steps of:digitally encoding said data into a plurality of data packets using atleast one encoder, each data packet having a MIMO preamble;simultaneously transmitting said packets using a plurality oftransmission antennas; based upon said MIMO preamble, sequentiallyestimating a carrier frequency offset (CFO), symbol timing, and channelresponse, wherein the estimated CFO refines a coarse CFO, and theestimated symbol timing refines a course symbol timing; and based uponsaid transmitted packets, generating said data at a receiving location.2. The method of claim 1, said digital encoding step using a channelcoding technique to implement forward error correction.
 3. The method ofclaim 2, wherein said channel coding technique is a convolutionalencoding technique.
 4. The method of claim 1, wherein said transmittingstep further comprises the step of: introducing temporal and spatialcorrelation into signals transmitted from different ones of saidplurality of transmission antennas using a spacetime coding technique.5. The method of claim 1, wherein said transmitting step furthercomprises the step of: scattering data bits of selective ones of saidpackets across a plurality of said antennas using a spatial interleavingtechnique.
 6. The method of claim 1, said generating step furthercomprising the steps of: receiving said packets using a plurality ofreceiving antennas; and digitally decoding said packets using a softdecoder.
 7. The method of claim 6, wherein said decoding step utilizesan unstructured least squares estimation technique.
 8. The method ofclaim 6, wherein said decoding step further comprises the step of:computing a signal to noise ratio of received signals that is usedduring said decoding process.
 9. The method of claim 1, wherein the stepof encoding comprises encoding each data packet so that the MIMOpreamble is backwardly compatible with a SISO system.
 10. The method ofclaim 1, the step of digitally encoding comprises encoding each datapacket so that the MIMO preamble comprises first and second longtraining sequences separated by a signal field, the second long trainingsymbol block comprising a first training sequence associated with afirst antenna and a second training sequence associated with a secondantenna, the first and second training sequences being orthogonal withrespect to one another.
 11. A method of wirelessly conveying datacomprising the steps of: digitally encoding said data into a pluralityof data packets using at least one encoder, each data packet having aMIMO preamble; simultaneously transmitting said packets using aplurality of transmission antennas; generating soft-information forimproving the performance of a convolutional decoding process, thesoft-information comprising (a) a set of unstructured least squaresestimates based upon channel gain between a plurality of transmittingantennas and a plurality of receiving antennas and (b) a variance oferror terms, the error terms corresponding to noise generated in a MIMOchannel; and based upon said transmitted packets, generating said dataat a receiving location.
 12. The method of claim 11, said digitalencoding step using a channel coding technique to implement forwarderror correction.
 13. The method of claim 12, wherein said channelcoding technique is a convolutional encoding technique.
 14. The methodof claim 11, wherein said transmitting step further comprises the stepof: introducing temporal and spatial correlation into signalstransmitted from different ones of said plurality of transmissionantennas using a space-time coding technique.
 15. The method of claim11, wherein said transmitting step further comprises the step of:scattering data bits of selective ones of said packets across aplurality of said antennas using a spatial interleaving technique. 16.The method of claim 11, said generating step further comprising thesteps of: receiving said packets using a plurality of receivingantennas; and digitally decoding said packets using a soft decoder. 17.The method of claim 16, wherein said decoding step utilizes anunstructured least squares estimation technique.
 18. A machine readablestorage having stored thereon a computer program for soft-detectingsoft-information required by a convolutional decoder in a wirelessmulti-input multi-output (MIMO) system, said computer program comprisinga set of instructions executable within a computing machine which whenexecuted cause the machine to perform the steps of: producing anunstructured least squares estimate for a time-varying channel in theMIMO system, said unstructured least square estimate being based uponchannel gains between a plurality of transmitting antennae and receivingantennae of the MIMO system; computing a SNR based on said producedunstructured least squares estimate; and providing to the convolutionaldecoder as the soft-information said unstructured least squares estimateand a variance of error terms, each of the error terms based upon thechannel gains and noise generated in a MIMO channel.
 19. A system forwireless conveying digital information comprising: a transmitting sourcehaving a plurality of transmitting antennas, wherein data is transmittedfrom said plurality of transmitting antennas simultaneously; a channelencoder communicatively linked to said transmitting source, said channelencoder configured to encode electronic documents into a plurality ofpackets, each packet having a MIMO preamble; a receiving mechanismhaving a plurality of receiving antennas; and a soft decodercommunicatively linked to said receiving mechanism for receiving a softinformation comprising a set of unstructured least squares estimates anda variance of error terms based upon MIMO channel noise.
 20. The systemof claim 19, wherein said transmitting source introduces temporal andspatial correlation into signals transmitted from different ones of saidplurality of transmission antennas using a space-time coding technique.21. The system of claim 19, wherein said channel encoder utilizes achannel coding technique to implement forward error correction.
 22. Thesystem of claim 21, wherein said channel coding technique is aconvolutional encoding technique.
 23. The system of claim 19, whereinsaid soft decoder calculates a plurality of unstructured least squaresestimates when decoding received signals.
 24. The system of claim 19,wherein said soft decoder calculates a signal to noise ratio of receivedsignals when decoding said received signals.
 25. The system of claim 19,further comprising: an interleaver communicatively linked to saidtransmitting source; and a deinterleaver communicatively linked to saidreceiving mechanism.
 26. A method of wirelessly conveying datacomprising the steps of: digitally encoding the data into a plurality ofdata packets using at least one encoder, each data packet having a MIMOpreamble that includes a first training symbol block and a secondtraining symbol block, wherein the second training symbol blockcomprises a first training sequence associated with a first antenna anda second training sequence associated with a second antenna;simultaneously transmitting said packets using a plurality oftransmission antennas; and based upon the MIMO preamble, performing atleast one of estimating a carrier frequency offset, determining a symboltiming, and generating a MIMO channel estimate.
 27. The method of claim26, wherein the step of digitally encoding comprises encoding each datapacket so that the first and second training sequences are orthogonal toone another.
 28. A method of wirelessly conveying data, the methodcomprising: estimating comparative gain for each of a plurality of pairsof transmitting and receiving antennas; generating a MIMO fat-fadingchannel representation; computing a plurality of unstructured leastsquares estimates and variance of error terms based on MIMO channelnoise; and supplying the unstructured least squares estimates andvariance to a decoder for performing a convolutional decoding processbased upon the supplying unstructured least squares estimates andvariance.